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    <abstract>In recent years there has been considerable interest in developing models for river and lake ecological systems, much of it directed toward progressively larger and more complex simulation models. However, such a trend causes concern about several important issues. In particular, relatively little attention has been paid to the problems of errors and uncertainty in the field data, of inadequate amount of field data, and of uncertainty about estimates of the model&apos;s parameters and about relations among the system&apos;s important variables. 

One possible approach to model uncertainty is a probabilistic interpretation of model predictions, generated by use of Monte-Carlo techniques. Fuzzy data sets and ranges are used. The resulting model response allows the derivation of measures for model credibility. Probability distributions can be computed for certain system states under (un)certain input conditions, representing the effects of insufficient data and structural uncertainty on model-based predictions. Such analysis indicates that prediction uncertainty increases, not only with the uncertainty in the data, but also with increasing &quot;distance&quot; from the empirical conditions, and with time.</abstract>
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    <publisher>RR-81-002. Reprinted from Helgoland Marine Research (Helgolaender Meeresuntersuchungen), 34:221-235 [1980].</publisher>
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    <iiasa_bibref>Reprinted from Helgoland Marine Research (Helgolaender Meeresuntersuchungen); 34:221-235 [1980]</iiasa_bibref>
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